These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

149 related articles for article (PubMed ID: 34353244)

  • 21. KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.
    Pandey P; Patel V; George NV; Mallajosyula SS
    J Proteome Res; 2018 Sep; 17(9):3214-3222. PubMed ID: 30032609
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance.
    Chowdhury AS; Reehl SM; Kehn-Hall K; Bishop B; Webb-Robertson BM
    Sci Rep; 2020 Nov; 10(1):19260. PubMed ID: 33159146
    [TBL] [Abstract][Full Text] [Related]  

  • 23. PredAPP: Predicting Anti-Parasitic Peptides with Undersampling and Ensemble Approaches.
    Zhang W; Xia E; Dai R; Tang W; Bin Y; Xia J
    Interdiscip Sci; 2022 Mar; 14(1):258-268. PubMed ID: 34608613
    [TBL] [Abstract][Full Text] [Related]  

  • 24. AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides.
    Zhang J; Zhang Z; Pu L; Tang J; Guo F
    IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(5):1831-1840. PubMed ID: 31985437
    [TBL] [Abstract][Full Text] [Related]  

  • 25. ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation.
    Cai L; Wang L; Fu X; Xia C; Zeng X; Zou Q
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33313672
    [TBL] [Abstract][Full Text] [Related]  

  • 26. sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure.
    Yan K; Lv H; Guo Y; Peng W; Liu B
    Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36342186
    [TBL] [Abstract][Full Text] [Related]  

  • 27. PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning.
    Wei L; Zhou C; Su R; Zou Q
    Bioinformatics; 2019 Nov; 35(21):4272-4280. PubMed ID: 30994882
    [TBL] [Abstract][Full Text] [Related]  

  • 28. ToxinPred 3.0: An improved method for predicting the toxicity of peptides.
    Rathore AS; Choudhury S; Arora A; Tijare P; Raghava GPS
    Comput Biol Med; 2024 Jul; 179():108926. PubMed ID: 39038391
    [TBL] [Abstract][Full Text] [Related]  

  • 29. HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information.
    Yang S; Xu P
    Anal Biochem; 2024 Jul; 690():115523. PubMed ID: 38552762
    [TBL] [Abstract][Full Text] [Related]  

  • 30. UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning.
    Charoenkwan P; Nantasenamat C; Hasan MM; Moni MA; Manavalan B; Shoombuatong W
    Int J Mol Sci; 2021 Dec; 22(23):. PubMed ID: 34884927
    [TBL] [Abstract][Full Text] [Related]  

  • 31. EnDL-HemoLyt: Ensemble Deep Learning-based Tool for Identifying Therapeutic Peptides with Low Hemolytic Activity.
    Sharma R; Shrivastava S; Singh SK; Kumar A; Singh AK; Saxena S
    IEEE J Biomed Health Inform; 2023 Apr; PP():. PubMed ID: 37018101
    [TBL] [Abstract][Full Text] [Related]  

  • 32. AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning.
    Lv H; Yan K; Guo Y; Zou Q; Hesham AE; Liu B
    Comput Biol Med; 2022 Jul; 146():105577. PubMed ID: 35576825
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides.
    Otović E; Njirjak M; Kalafatovic D; Mauša G
    J Chem Inf Model; 2022 Jun; 62(12):2961-2972. PubMed ID: 35704881
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models.
    Lobo F; González MS; Boto A; Pérez de la Lastra JM
    Int J Mol Sci; 2023 Jun; 24(12):. PubMed ID: 37373415
    [TBL] [Abstract][Full Text] [Related]  

  • 35. sAMP-PFPDeep: Improving accuracy of short antimicrobial peptides prediction using three different sequence encodings and deep neural networks.
    Hussain W
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34849586
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides.
    Xu J; Li F; Leier A; Xiang D; Shen HH; Marquez Lago TT; Li J; Yu DJ; Song J
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33774670
    [TBL] [Abstract][Full Text] [Related]  

  • 37. MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism.
    Liang X; Zhao H; Wang J
    Protein Sci; 2024 Apr; 33(4):e4966. PubMed ID: 38532681
    [TBL] [Abstract][Full Text] [Related]  

  • 38. PPTPP: a novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning.
    Zhang YP; Zou Q
    Bioinformatics; 2020 Jul; 36(13):3982-3987. PubMed ID: 32348463
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Machine learning assisted design of highly active peptides for drug discovery.
    Giguère S; Laviolette F; Marchand M; Tremblay D; Moineau S; Liang X; Biron É; Corbeil J
    PLoS Comput Biol; 2015 Apr; 11(4):e1004074. PubMed ID: 25849257
    [TBL] [Abstract][Full Text] [Related]  

  • 40. E-CLEAP: An ensemble learning model for efficient and accurate identification of antimicrobial peptides.
    Wang SC
    PLoS One; 2024; 19(5):e0300125. PubMed ID: 38722967
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 8.